Mathematical and computational operators will be developed from the basis of the autocorrelation function to reveal regular structure and symmetry in neurobiological images. The same approach will be taken with artificial images as a probe of the neural image processing by the brain. Having generalized the autocorrelation function to higher order relationships, it will then be modified in three different ways. The first is transformation of the image space prior to multiplication within the integral; the second is nonlinear transformation of the luminance function of the image (including a power nonlinearity which can operate as a peak detector as opposed to the usual energy summation within the autocorrelation integral); and the last is the inclusion of a filter operator within the integral. In the first class, specific emphasis will be placed on the spatial inversion operation, which then results in an efficient mathematical operator for detecting for reflection symmetry. This operator will be used for symmetry detection in neurobiological structures, and as a basis for a neural model of human symmetry detection, as measured by a psychophysical paradigm. It will also be applied as a method of disambiguating the extraction of stereoscopic depth in complex images by means of the inherent symmetry in binocular disparity arrays. In the second class, efforts will focus on developing a sign-stratified autocorrelation analysis to help capture local features in textures and mapping images. This will also serve as a model for the rectification aspect of human cortical cell responses, which will be evaluated with specialized textures designed to probe higher-order attributes of human image processing. The third class, 2D convolution with known receptive field properties of simple, complex and hypercomplex cells will be applied to determine the array stimulation profiles for noteworthy visual stimuli. This process will allow the generation and testing of new models of the mechanisms of human perceptual distortions.